Method and equipment for regulating dynamically an average area of a channel estimation

ABSTRACT

The present invention relates to a method and an apparatus for adjusting an average interval of channel estimation dynamically based on Doppler-shift. The method comprises the steps of estimating Doppler-shift by using level cross rate (LCR) according to differently moving speeds of mobile terminals, determining the optimal average interval of channel estimation based on the relationship between the existing Doppler-shift and the optimal average interval of channel estimation, dynamically adjusting the average interval of channel estimation according to the determined the optimal average interval of channel estimation to make the coherent receiver obtain the optimal estimation performance at different moving speeds. The apparatus of the present invention comprises a channel estimation module, a valid/strongest finger selection module, a RAKE demodulation and combining module, a RAKE output decision module, an LCR detection and Doppler-shift estimation module, a Gaussian noise power estimation module, a strongest path signal power estimation module, and an optimal average interval calculation module.

FIELD OF THE INVENTION

This invention relates to CDMA (Code Division Multiple Access) cellularcommunication field, especially to the technique of dynamicallyadjusting an average interval of Doppler-shift estimation and channelestimation in WCDMA (Wideband CDMA) and CDMA2000-1×.

DESCRIPTION OF THE PRIOR ART

Compared with FDMA (Frequency Division Multiple Access) and TDMA (TimeDivision Multiple Access), CDMA has advantages such as large capacity,anti-multipath fading and high frequency band efficiency, and has becomethe main technology of radio transmission for 3G (third generation)mobile communications. Spread-spectrum signal receivers of CDMA aredivided into two types, i.e., coherent receivers and non-coherentreceivers. The coherent receiver needs to know the phase information ofreceived signal, while the non-coherent receiver does not, but itrequires that the transmitted signal use orthogonal modulation mode. Atpresent, most of receivers adopt coherent receiving mode. This inventionmainly focuses on the coherent receiving mode which is predominant in 3Gmobile communication standards.

Signal fading exists in the multipath transmission environment of mobilechannels. The amplitudes and phases of received signals are time-varyingdue to the signal fading. In CDMA cellular mobile communication systems,which adopt spread-spectrum technology, the amplitude and phaseinformation of multipath signals can be estimated through receiving thecontinuous pilot signals with deterministic information. It is thereforepossible to achieve multpath diversity and coherent reception. Thecoherent spread-spectrum receiver that performs diversity process formultipath fading signals is called RAKE receiver. RAKE receivers canperform phase-correction and maximum ratio combining for multipathsignals which carry the same information and their fadingcharacteristics are mutually independent, thereby overcoming themultipath fading and improving the received SNR (Signal-to-Noise Ratio).

It is necessary to estimate the time-varying parameters of fadingchannels so as to perform coherent reception. This process is calledchannel estimation. The conventional method decorrelates each path ofreceived signals by means of the known pilot symbols respectively,estimates a plurality of sampled values of channel parameters andaverages the plurality of sampled values to obtain more accurate channelestimation values. In the case where channel parameters keep constantrelatively, larger average interval leads to more accurate channelestimation results. In practice, however, channel parameters change withthe Doppler-shift of fading channels. The faster the moving speed ofmobile terminal is, the larger Doppler-shift is, and the faster thechannel parameters change. In other words, the interval in which channelparameters relatively keep constant changes with the moving speed of themobile terminal (Doppler-shift). The faster the moving speed is, thesmaller the interval is, and vice versa. The existing mobile terminalsare hard to apply in different mobile environments if fixed averageinterval is adopted. According to the requirements of 3G mobilecommunication system, mobile terminals should be able to adapt to thedynamic change in environment such as from static state to moving stateof 500 Kilometers per hour. Therefore, in order to obtain optimalreceiving performance, mobile terminals should have the abilities ofestimating Doppler-shift and adjusting the average interval of channelestimation dynamically according to Doppler-shift.

SUMMARY OF THE INVENTION

The object of the present invention is to provide a method and anapparatus for adjusting the average interval of channel estimationdynamically based on Doppler-shift estimation, so that the disadvantage,which a fixed average interval is not able to track the speed of amobile terminal, can be overcome.

The method of the present invention dynamically adjusts the optimalaverage interval of channel estimation based on Doppler-shift. Itestimates Doppler-shift by using level crossing rate (LCR) according tovaraint moving speeds of a mobile terminal, and then, the optimalaverage interval of the channel estimation can be determined based onthe relationship between existing Doppler-shift and the averageinterval.

According to an aspect of this invention, a method of dynamicallyadjusting an average interval of the channel estimation consists of thefollowing steps: calculating the average level crossing rate (LCR);estimating the channel parameters of the effective arriving paths andthe envelope signals thereof by using coherent RAKE reception;calculating the average LCR of the envelope signals of effectivearriving path, and estimating the Doppler-shift of channels accordingly;calculating the optimal average interval of channel estimation undercurrent channel environments according to the relationship between theexisting Doppler-shift and the optimal average interval of channelestimation; dynamically adjusting the average interval of channelestimation according to the calculated the optimal average interval ofchannel estimation to make the coherent receiver obtain the optimalestimation performance at different moving speeds.

According to another aspect of this invention, an apparatus fordynamically adjusting an average interval of channel estimationcomprises a channel estimation module; a valid/strongest fingerselection module; a RAKE demodulating and combining module; a RAKEoutput decision module; an LCR detecting and Doppler estimating module;a Gaussian noise power estimating module; a strongest path signal powerestimating module; and an optimal average interval calculating module.

DESCRIPTION OF THE DRAWINGS

Other advantages and features of the present invention will becomereadily apparent from the following detailed description of theinvention and the embodiments thereof, from the accompanying drawings,in which

FIG. 1 is a timing chart illustrating the level crossing rate (LCR) andthe duration of average level;

FIG. 2 is a schematic diagram illustrating the average channelestimation based on the moving average window;

FIG. 3 is a block diagram illustrating the RAKE receiver of cdma2000-1×system based on the principle of LCR according to an embodiment of theinvention; and

FIG. 4 is a schematic diagram illustrating the comparison of theperformance between fixed average interval and dynamic average intervalused at different moving speeds.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Firstly, Doppler-shift estimation of CDMA systems in a multipath fadingchannel environment is described. It is well known from the fadingcharacteristics of wireless channels that the fading rate is relative tothe fading depth. The number of times which occur deep-fading isrelatively low, and the number of times which occur shallow-fading isquite frequent. Level crossing rate (LCR) quantitatively describes theparameters of this characteristic. Level crossing rate indicates theaverage number of times which the signal envelop crosses a certainspecified level with positive (or negative) slope within a time unit. Asshown in FIG. 1, in the fixed interval T, the received signal envelopecrosses the average level R with positive slope at the time points 1, 2,3, . . . , and M, i.e. the rate of the signal level below R is M becauseof channel fading. The LCR is N_(A)=M/T. In the case where theobservation interval is long enough, Doppler-shift resulting from thechannel fading can be estimated from equation 1 as follows:f _(d) ≈M/T  (Equation 1)where f_(d) represents Doppler-shift.

In coherent reception of the CDMA system, the continuous pilot channelwhich is used to transmit the known pilot sequence in advance can beused for system timing, carrier extraction, channel estimation,handover, etc. It is assumed that the total number of effective arrivingpaths (fingers) which can be distinguished by a RAKE receiver of CDMAsystem is L, after the received signal passes the RAKE receiver, theoutput signal of the lth finger after the despreading operation is fromequation 2 as follow:γ_(l)(n)=d _(p)(n)c _(l)(n)+v(n)  (Equation 2)where r_(l)(n) is the output signal of the lth finger, d_(p)(n) is thetransmitted pilot symbol, c_(l)(n) is the channel parameter of the nthsymbol in the ith finger, v(n) is complex Additive White Gaussian Noise(AWGN). Based on the pilot symbols, the estimated channel parametersequence is:ĉ _(l,s)=γ_(l)(n)d _(p) ^(*)(n)/|d _(p)(n)|² =c _(l)(n)+z(n)  (Equation3)where ĉ_(l,s)(n) is the estimated instantaneous channel coefficient ofc_(l)(n), z(n) is the estimated white noise introduced by v(n), thevariance of z(n) is obtained by averaging above symbol instantaneousestimating value by σ_(n) ² with window sliding, as shown in FIG. 2. Itis assumed that the window length is P, the more accurate estimatedvalues for channel parameters can be then obtained as follows.$\begin{matrix}{{{\hat{c}}_{l}(n)} = {\frac{1}{P}{\sum\limits_{i = {- N_{1}}}^{N_{2}}{{\hat{c}}_{l,s}\left( {n - i} \right)}}}} & \left( {{Equation}\quad 4} \right)\end{matrix}$in equation 4, P=N₁+N₂+1 is the window length for moving averaging,whose initial value should keep the instantaneous channel estimationvalue, ĉ_(l,s)(n−i),−N₁≦i≦N₂, to be a constant relatively.

It is necessary to calculate the average level of its channel estimationin order to calculate the Doppler-shift of the ith finger of RAKEreceiver. Therefore, the envelop average value of ĉ_(l)(n) iscontinuously sampled for K times as the required average levelestimation value: $\begin{matrix}{{{{\hat{R}}_{l}(n)} = {\frac{1}{K}{\sum\limits_{i = 0}^{K}{{{\hat{c}}_{l}\left( {n - i} \right)}}}}}{or}{{{\hat{R}}_{l}(n)} = {{{\hat{R}}_{l}\left( {n - 1} \right)} + {\frac{1}{K}\left\{ {{{{\hat{c}}_{l}(n)}} - {{{\hat{c}}_{l}\left( {n - K} \right)}}} \right\}}}}} & \left( {{Equation}\quad 5} \right)\end{matrix}$where value K in the equation 5 should be large enough to make thecorresponding observation length far more larger than the channel fadingperiod. It is assumed that the time period to which the K continuousĉ_(l)(n) correspond is T, and the number of times which the envelop|ĉ_(l)(n)|for channel estimation of the ith path upwardly crosses theaverage level {circumflex over (R)}_(l)(n) is M_(l)(n) times, thus, theestimated Doppler-shift {circumflex over (f)}_(d,l)(n) for the ithfinger of the RAKE receiver is calculated from equation (6) as follows.{circumflex over (f)} _(d,l)(n)≈M _(l)(n)/T  (Equation 6)After that, the optimal average interval of channel estimation undercurrent channel conditions such as Doppler-shift and noise is calculatedwith the relationship between existing Doppler-shift and averageinterval of the optimal average interval of channel estimation. IfDoppler-shift is {circumflex over (f)}_(d,l), the is pilot symbolduration is T_(S), the variance of Gaussian White Noise is σ_(n) ² andthe power for the lth arriving path is σ_(l) ², the optimal averageinterval of channel estimation is then calculated based on the followingequation. $\begin{matrix}{P = \sqrt[5]{\left( {384\quad{\sigma_{l}^{2}/\left( {\left( {2\quad\pi\quad f_{d}T_{s}} \right)^{4}\quad\sigma_{n}^{2}} \right)}} \right.}} & \left( {{Equation}\quad 7} \right)\end{matrix}$

Since the transmitted data is performed simultaneously with the codechannels, which are parallel with the pilot channel, the channel fadingparameters are the same as the those estimated by the pilot channel.After despreading, the lth finger output of the received data channelcan be given in equation (8)r _(l) ^(d)(m)=d(m)c _(l)(n)+v(m), m=qn+j, j=0,1,Λ,q−1  (Equation 8)where d(m) is the transmitted symbol and q is the number of thetrasnmitted symbols included in a pilot symbol duration. The maximumratio combining output of a RAKE receiver is then obtained as follows:$\begin{matrix}{{\hat{d}(m)} = {\sum\limits_{l = 1}^{L}{{r_{l}^{d}(m)}{{\hat{c}}_{l}^{*}(n)}}}} & \left( {{Equation}\quad 9} \right)\end{matrix}$

The variance σ_(n) ² of Gaussian White Noise and the signal power σ_(l)² of the ith path can be estimated respectively from above equations asfollows. $\begin{matrix}\begin{matrix}{{{{\hat{\sigma}}_{n}^{2}(m)} = {\frac{1}{q\quad K}{\sum\limits_{i = 0}^{{q\quad K} - 1}{{{r_{l}^{d}\left( {m - i} \right)} - {{\hat{d}\left( {m - i} \right)}{{\hat{c}}_{l}\left( {n - k} \right)}}}}^{2}}}},} \\{k = \left\lfloor {i/q} \right\rfloor} \\{{or}\quad} \\{{{\hat{\sigma}}_{n}^{2}(m)} = \begin{matrix}{{{\hat{\sigma}}_{n}^{2}\left( {m - 1} \right)} + {\frac{1}{q\quad K}\left\{ {{{{r_{l}^{d}(m)} - {{\hat{d}(m)}{{\hat{c}}_{l}(n)}}}}^{2} -} \right.}} \\\left. {{{r_{l}^{d}(m)} - {{\hat{d}\left( {m - {q\quad K}} \right)}{{\hat{c}}_{l}\left( {n - K} \right)}}}}^{2} \right\}\end{matrix}}\end{matrix} & \left( {{Equation}\quad 10} \right) \\\begin{matrix}{{{\hat{\sigma}}_{l}^{2}(n)} = {\frac{1}{K}{\sum\limits_{i = 0}^{K - 1}{{{\hat{c}}_{l}\left( {n - i} \right)}}^{2}}}} \\{{or}\quad} \\{{{\hat{\sigma}}_{l}^{2}(n)} = {{{\hat{\sigma}}_{l}^{2}\left( {n - 1} \right)} + {\frac{1}{K}\left\{ {{{{\hat{c}}_{l}(n)}}^{2} - {{{\hat{c}}_{l}\left( {n - K} \right)}}^{2}} \right\}}}}\end{matrix} & \left( {{Equation}\quad 11} \right)\end{matrix}$Then, the average interval of channel estimation is dynamically adjustedbased on the calculated optimal average interval of channel estimation.This enables the coherent receivers to obtain the optimal estimatingperformance at different moving speeds. For the l^(th) finger of channelestimator, 0 l L−1, the optimal average interval of channel estimationis iteratively performed based on the following method:

-   -   Step 1: Initiating the parameters, and setting the initial value        of average interval parameter as P=P₀. The iteration number is        n=1;    -   Step 2: Calculating the instantaneous channel estimation        ĉ_(l,s)(n) to which each pilot symbol corresponds and setting        P=P_(n−1), calculating the average channel estimation of a        sliding window ĉ_(l)(n) according to equation (4), and then        calculating the average level {circumflex over (R)}_(l)(n)        according to equation (5);    -   Step 3: Counting statistics for average channel estimation        ĉ_(l)(n−i), where 0 i K−1 in K estimation areas (i.e. T time        interval) and the times for up-cross average level A, (n) that        ĉ_(l)(n−i) corresponds to, where 0 i K−1; and estimating        Doppler-shift {circumflex over (f)}_(d,l)(n) of mobile station        according to (equation 6):    -   Step 4: Calculating the variance of Gaussian White Noise σ_(n) ²        as well as the estimated value for the signal power σ_(n) ² of        the ith path according to equation (10) and equation (11)        respectively, and calculating the optimal average length P_(n)        of the channel estimator when Doppler-shift is {circumflex over        (f)}_(d,l)(n) according to equation (7); n←n−1, and then        returning back to Step 2.

The iteration method described above is performed on the ith path of theRAKE receiver. Since the effects of Doppler-shift caused by mobileterminals on the arriving path for each channel are the same, Theiteration method described above can be simplified to determine theoptimal average interval of channel estimation of all L pathes only byusing the strongest signal arriving path.

The advantages of this invention are given as follows:

The method and the apparatus of the present invention can be easilyachieved by adding a RAKE output decision module A100, an LCR detectionand Doppler-shift estimation module A101, a Gaussian noise powerestimation module A102, a strongest path signal power estimation moduleA103, and an optimal average interval calculation module A104 to aconventional RAKE receiver which is composed of a channel estimationmodule B101, a valid/strongest finger selection module B102 and a RAKEdemodulation and combination module B103 (referring to FIG. 3). Thedynamic adjusting apparatus according to the present invention can beused in any CDMA mobile communication system with continuous pilotchannel, including 3GPP WCDMA and 3GPP2 cdma2000 systems. Further, theapplication of the present invention can make RAKE receivers achieveoptimal performance at different moving speeds. The change of channelscan be automatically traced when the moving speed is changed.Furthermore, an optimal average length can be selected within theestimated speed range to improve the system performance to a largeextent.

The invention will be described in detail based on the followingembodiments with reference to the attached drawings which do not intendto limit the scope of the present invention.

Referring to FIG. 1, a schematic diagram illustrating the signal envelopcrossing the level R is shown. Level Crossing Rate (LCR) indicates theaverage number of times for signal envelop to cross the level R withpositive slope, for example, the point which the positive slopeintersects with the level R as shown by referring sign 1 in FIG. 1,within a unit of time T.

FIG. 2 illustrates the average channel estimation based on windowsliding.

The method according to this invention can be applied to any CDMA mobilecommunication systems with continuous pilot channel, including 3GPP (TheThird Generation Partnership Project) WCDMA and 3GPP2 (The ThirdGeneration Partnership Project 2) cdma2000 systems.

The apparatus according to the present invention can be implemented byadding a RAKE output decision module, an LCR detection and Doppler-shiftestimation module, a Gaussian noise power estimation module, a strongestpath signal power estimation module, and an optimal average intervalcalculation module, i.e., five computing units, to a conventional RAKEreceiver.

FIG. 3 is a block diagram illustrating a RAKE receiver in cdma2000-1×system based on LCR principle according to an embodiment of thisinvention. The configuration of the apparatus to which the method ofthis invention is applied is shown in detail in this figure. As shown inFIG. 3, the RAKE receiver of the invention comprises a channelestimation module B101, a valid/strongest finger selection module B102,a RAKE demodulation and combination module B103, a RAKE output decisionmodule A100, an LCR detection and Doppler-shift estimation module A101,a Gaussian noise power estimation module A102, a strongest finger signalpower estimation module A103, and an optimal average intervalcalculation module A104. The channel estimation module B101 receives thebaseband sampling signals and performs the correlation operation. Thevalid/strongest finger selection module B102 selects the effectivesignal arriving path and the strongest signal arriving path according tothe amplitude of the channel estimation module B101. The RAKEdemodulation and combination module B103 receives the baseband samplingsignal and performs the correlation operation. The RAKE output decisionmodule A100 receives the output from the RAKE demodulation andcombination module B103 and performs hard-decision on the output fromthe RAKE receiver. The LCR detection and Doppler-shift estimation moduleA101 receives the output from the valid/strongest finger selectionmodule B102 and calculates the average level of the signals from thisfinger. The Gaussian noise power estimation module A102 calculates thenoise power included in the strongest signal arriving finger. Thestrongest path signal power estimation module A103 receives thestrongest finger channel estimation resulting from the valid/strongestfinger selection module B102. The optimal average interval calculationmodule A104 adjusts the average interval of channel estimator.

As shown in FIG. 3, the valid/strongest finger selection module B102connects to the LCR detection and Doppler-shift estimation module A101and to the strongest finger signal power estimation module A103respectively. The RAKE demodulation and combination module B103 connectsto the Gaussian noise power estimation module A102 and the RAKE outputdecision module A100 respectively.

The operation of apparatus according to the present invention will bedescribed in detail as follows. Firstly, the received baseband analogsignals are A/D converted to digital signals, and the converted digitalsignals are input into the channel estimation module B101. The channelestimation module B101 receives the digital baseband sampling signalsand then performs the correlation operation. That is, the channelestimation module B101 estimates the instantaneous channel parameterĉ_(l,s)(n) according to equation (3) and calculates the sliding averagechannel estimation value ĉ_(l)(n) according to equation (4), where l=0,1, . . . , L−1, and value L should be larger than the multipath fadingdelay spread. Thus, multipath fading channels can be estimated. Forcdma2000-1× systems, the selection of the value L is 32. At thebeginning of the iteration, the selection of the average length P may be4 symbols (i.e., 256 chips), and the iterated result is sent to thevalid/strongest finger selection module B102. The valid/strongest fingerselection module B102 selects the effective signal arriving finger andthe strongest signal arriving finger according to the amplitude of thechannel estimating value c, (n) from the channel estimation module B101.The strongest signal arriving finger is used to determine the, followingaverage level, LCR, the power of the arriving signal path and the noisepower so that the value of path 1 can be calculated as needed. Theeffective signal arriving path is used to the calculation of thefollowing RAKE combination. The valid/strongest finger selection moduleB102 send the selected effective signal arriving path and the strongestsignal arriving path to the RAKE demodulation and combination moduleB103, the strongest finger signal power estimation module A103 and theLCR detection and Doppler-shift estimation module A101. The RAKEdemodulation and combination module B103 receives the baseband samplingsignals and performs the correlation operation to obtain thedemodulation output expressed as follows,r _(l) ^(d)(m)=d(m)c _(l)(n)+v(m), m=qn+j, j=0,1,Λ,q−1Then, the maximum ratio combining is performed according to equation (9)so that the output from the RAKE receiver is obtained. On the otherhand, the demodulation output from the strongest finger and the outputdecision from the RAKE receiver are sent to the Gaussian noise powerestimation module A102. The RAKE output decision module A100 receivesthe output from the RAKE demodulation and combination module B103 andperforms hard-decision on the output from the RAKE receiver and, obtainsthe judgment estimation value d(m) of the transmitted data symbol. Then,the result is supplied to the Gaussian noise power estimation moduleA102. The LCR detection and Doppler-shift estimation module A101receives the output from the valid/strongest finger selection moduleB102, and calculates the average level {circumflex over (R)}_(l)(n) ofthe path signal according to equation (5), and carries out the levelcrossing rate detection as shown in FIG. 1. Furthermore, The LCRdetection and Doppler-shift estimation module A101 counts the number oftimes which the envelop upwardly crosses the average level {circumflexover (R)}₁(n) within the time intervals of continuous K of ĉ₁(n−i)S; andthen calculates the estimation value {circumflex over (f)}_(d,l)(n) ofDoppler-shift according to equation (6); and sends the result to theoptimal channel estimation average interval calculation module A104. TheGaussian noise power estimation module A102 receives the demodulationoutput of the strongest finger signal from the RAKE demodulation andcombination module B103 and the output from the RAKE output decisionmodule A100, and calculates the noise power included in the strongestsignal arriving finger. That is, the Gaussian noise power estimationmodule A102 calculates the noise power estimation {circumflex over(σ)}_(l) ² of the strongest finger signal according to equation (10),and sends the result to the optimal average interval calculation moduleA104. The strongest finger signal power estimation module A102 receivesthe strongest finger channel estimation result from the valid/strongestfinger selection module B102, calculates the strongest finger powerestimation {circumflex over (σ)}_(l) ² according to equation (11) andsends the result to the optimal average interval calculation moduleA104. The optimal average interval calculation module A104 receives theoutputs from the LCR detection and Doppler-shift estimation module A101,the Gaussian noise power estimation module A102 and the strongest fingersignal power estimation module A103 respectively, and selects theoptimal average interval according to equation (7), and sends the resultto the channel estimation module B101, thereby the average intervallength P of the channel estimator is adjusted.

FIG. 4 shows a typical calculation result which the average interval ofchannel estimation is dynamically adjusted in a mobile terminal used for3GPP2 cdma2000-1× systems. The channel model adopted here is the M. 1225urban channel model as recommended by ITU-R, the calculation parametersthereof are given as follows: E_(b)/N₀=3 dB; data transmission rate is9.6 kbps; carrier frequency is 800 MHz frequency as recommended by 3GPP2standard. As shown in FIG. 4, if a fixed average interval (512 chips)with 5 pilot symbols is used, a good performance can be obtained withinthe range which the equivalent moving speed is from 100 km/h to 200km/h. If channel estimation average interval to which dynamic adjustmentis applied is used, the overall performance of the RAKE receivers can beoptimized all the time, and the RAKE receivers may adapt to differentmoving speeds. In general, when the speed is less than 100 km/h, theoptimal average interval is from 8 to 16 symbols (from 512 to 1024chips). When the speed is changed within the range from 100 km/h to 300km/h, the optimal average interval is 8 symbols (512 chips). When thespeed is larger than 300 km/h, the optimal average interval is from 4 to6 symbols (from 384 to 256 chips).

Although the present invention has been described by means of exemplaryembodiments, it should be understood that many changes and substitutionsmay further be made by those skilled in the art without departing fromthe scope of the present invention which is defined by the appendedclaims.

1. A method of dynamically adjusting the average interval of channelestimation, comprising steps of: calculating the average level crossrate (LCR); estimating the channel parameters of the effective arrivingpath and the envelope signals thereof by using coherent RAKE reception;calculating the average LCR of the envelope signals of the effectivearriving path, and estimating the Doppler-shift of channels accordingly;calculating the optimal average interval of channel estimation undercurrent channel environment according to the relationship between theexisting Doppler-shift and the optimal average interval of channelestimation; and dynamically adjusting the average interval of channelestimation according to the calculated optimal average interval ofchannel estimation to make the coherent receiver obtain the optimalestimation performance at different moving speeds.
 2. The methoddynamically adjusting the average interval of a channel estimationaccording to claim 1, wherein the step of calculating the optimalaverage interval of channel estimation further comprises: step 1:initiating the parameters, and setting the initial value of averageinterval parameter as P=P₀, the number of iteration is n=1; step 2:calculating the instantaneous channel estimation ĉ_(l,s)(n) to whicheach pilot symbol corresponds and setting P=P_(n−1), calculating theaverage channel estimation of a sliding window ĉ_(l)(n) using theequation expressed as follows${{\hat{c}}_{l}(n)} = {\frac{1}{P}{\sum\limits_{i = {- N_{1}}}^{N_{2}}{{\hat{c}}_{l,s}\left( {n - i} \right)}}}$ and calculating the average level {circumflex over (R)}_(l)(n) usingthe following equation${{\hat{R}}_{l}(n)} = {\frac{1}{K}{\sum\limits_{i = 0}^{K}{{{\hat{c}}_{l}\left( {n - i} \right)}}}}$or${{{\hat{R}}_{l}(n)} = {{{\hat{R}}_{l}\left( {n - 1} \right)} + {\frac{1}{K}\left\{ {{{{\hat{c}}_{l}(n)}} - {{{\hat{c}}_{l}\left( {n - K} \right)}}} \right\}}}};$step 3: calculating statistics for average channel estimationĉ_(l)(n−i), where 0 i K−1 in K estimation intervals (i.e. T timeinterval) and the times for up-cross average level {circumflex over(R)}_(l)(n) that ĉ_(l)(n−i) corresponds to, where 0 i K−1; andestimating Doppler-shift Id, (n) of mobile station according tofollowing equation:{circumflex over (f)} _(d,l)(n)≈M _(l)(n)/T; step 4: calculating thevariance of Gaussian White Noise τ_(n) ² with the following equation${{{\hat{\sigma}}_{n}^{2}(m)} = {\frac{1}{q\quad K}{\sum\limits_{i = 0}^{{q\quad K} - 1}{{{r_{l}^{d}\left( {m - i} \right)} - {{\hat{d}\left( {m - i} \right)}{{\hat{c}}_{l}\left( {n - k} \right)}}}}^{2}}}},{k = \left\lfloor {i/q} \right\rfloor}$or ${{\hat{\sigma}}_{n}^{2}(m)} = \begin{matrix}{{{\hat{\sigma}}_{n}^{2}\left( {m - 1} \right)} + {\frac{1}{q\quad K}\left\{ {{{{r_{l}^{d}(m)} - {{\hat{d}(m)}{{\hat{c}}_{l}(n)}}}}^{2} -} \right.}} \\\left. {{{r_{l}^{d}(m)} - {{\hat{d}\left( {m - {q\quad K}} \right)}{{\hat{c}}_{l}\left( {n - K} \right)}}}}^{2} \right\}\end{matrix}$ and calculating the estimated value for the signal powerσ_(l) ² of the ith path with the following equation${{\hat{\sigma}}_{l}^{2}(n)} = {\frac{1}{K}{\sum\limits_{i = 0}^{K - 1}{{{\hat{c}}_{l}\left( {n - i} \right)}}^{2}}}$or${{\hat{\sigma}}_{l}^{2}(n)} = {{{\hat{\sigma}}_{l}^{2}\left( {n - 1} \right)} + {\frac{1}{K}\left\{ {{{{\hat{c}}_{l}(n)}}^{2} - {{{\hat{c}}_{l}\left( {n - K} \right)}}^{2}} \right\}}}$and calculating the optimal average length P_(n) of the channelestimator when Doppler-shift is {circumflex over (f)}_(d,l)(n) with thefollowing equation${P = \sqrt[5]{\left( {384\quad{\sigma_{l}^{2}/\left( {\left( {2\quad\pi\quad f_{d}T_{s}} \right)^{4}\quad\sigma_{n}^{2}} \right)}} \right.}},$n←n−1, and then returning back to Step
 2. 3. An apparatus fordynamically adjusting an average interval of channel estimation,comprising a channel estimation module, a valid/strongest fingerselection module, and a RAKE demodulation and combining module,characterized in that further comprising a RAKE output decision module;an LCR detecting and Doppler-shift estimation module; a Gaussian noisepower estimation module; a strongest path signal power estimationmodule; and an optimal average interval calculation module, wherein thevalid/strongest finger selection module connecting to the LCR detectionand Doppler-shift estimation module and to the strongest path signalpower estimation module respectively; the RAKE demodulation andcombining module connecting to the Gaussian noise power estimationmodule and the RAKE output decision module A100 respectively.
 4. Theapparatus for dynamically adjusting an average interval of channelestimation according to claim 3, wherein the channel estimation modulereceives the baseband sampling signals and performs the correlationoperation; the valid/strongest finger selection module selects theeffective signal arriving path and the strongest signal arriving pathaccording to the amplitude of the channel estimation module; the RAKEdemodulation and combining module receives the baseband sampling signaland performs the correlation operation; the RAKE output decision modulereceives the output from the RAKE demodulation and combining module andperforms hard-decision on the output from the RAKE receiver; the LCRdetection and Doppler-shift estimation module receives the output fromthe valid/strongest finger selection module and calculates the averagelevel of the signals from this path; the Gaussian noise power estimationmodule calculates the noise power included in the strongest signalarriving path; the strongest path signal power estimation modulereceives the strongest path channel estimation result from thevalid/strongest finger selection module; and the optimal averageinterval calculation module adjusts the average interval of the channelestimator.